328 citations · 1.2k across the 57 of their papers we have counts for
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GraphACT: Accelerating GCN Training on CPU-FPGA Heterogeneous Platforms
Hanqing Zeng, Viktor Prasanna
Graph Convolutional Networks (GCNs) have emerged as the state-of-the-art deep learning model for representation learning on graphs. It is challenging to accelerate training of GCNs…
SPEC2: SPECtral SParsE CNN Accelerator on FPGAs
Yue Niu, Hanqing Zeng, Ajitesh Srivastava +4
To accelerate inference of Convolutional Neural Networks (CNNs), various techniques have been proposed to reduce computation redundancy. Converting convolutional layers into freque…
Building HVAC Scheduling Using Reinforcement Learning via Neural Network Based Model Approximation
Chi Zhang, Sanmukh R. Kuppannagari, Rajgopal Kannan +1
Buildings sector is one of the major consumers of energy in the United States. The buildings HVAC (Heating, Ventilation, and Air Conditioning) systems, whose functionality is to ma…
GraphSAINT: Graph Sampling Based Inductive Learning Method
Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava +2
Graph Convolutional Networks (GCNs) are powerful models for learning representations of attributed graphs. To scale GCNs to large graphs, state-of-the-art methods use various layer…
Planting Trees for scalable and efficient Canonical Hub Labeling
Kartik Lakhotia, Qing Dong, Rajgopal Kannan +1
Point-to-Point Shortest Distance (PPSD) query is a crucial primitive in graph database applications. Hub labeling algorithms compute a labeling that converts a PPSD query into a li…